By Qingsong Xu, Kok Kiong Tan

ISBN-10: 1848000200

ISBN-13: 9781848000209

ISBN-10: 1848824246

ISBN-13: 9781848824249

ISBN-10: 1852331380

ISBN-13: 9781852331382

ISBN-10: 1852333383

ISBN-13: 9781852333386

ISBN-10: 3319066161

ISBN-13: 9783319066165

ISBN-10: 3319216228

ISBN-13: 9783319216225

ISBN-10: 3319216236

ISBN-13: 9783319216232

This e-book explores rising tools and algorithms that permit distinctive keep watch over of micro-/nano-positioning platforms. The textual content describes 3 keep an eye on techniques: hysteresis-model-based feedforward keep watch over and hysteresis-model-free suggestions regulate in accordance with and loose from kingdom commentary. every one paradigm gets devoted realization inside of a specific a part of the text.

Readers are proven the right way to layout, validate and practice numerous new keep an eye on ways in micromanipulation: hysteresis modelling, discrete-time sliding-mode keep watch over and model-reference adaptive keep an eye on. Experimental effects are supplied all through and increase to an in depth remedy of sensible functions within the fourth a part of the ebook. The purposes specialise in keep watch over of piezoelectric grippers.

Advanced keep watch over of Piezoelectric Micro-/Nano-Positioning Systems will support educational researchers and training regulate and mechatronics engineers drawn to suppressing resources of nonlinearity corresponding to hysteresis and flow while combining place and strength keep an eye on of precision platforms with piezoelectric actuation.

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Additional resources for Advanced Control of Piezoelectric Micro-/Nano-Positioning Systems

Example text

N n+1 j d ∗j = max |y(t)|, for j = 0, 1, . . 39) where u and y denote the voltage input and displacement output data of the piezostage system, respectively. In addition, the initial states are assigned as y0i = 0, i = 0, 1, . , n, for simplicity. 9925 Then, the weights wh and w∗s are identified by solving an optimization problem of minimizing the model error: E[u, y](wh , w∗s , t) = whT Hr [u, y0 ](t) − w∗s T S∗d [y](t). 40) Here, instead of using L 22 -norm optimization [10], the weight parameters are identified by resorting to the PSO approach.

Springer, Berlin (2013) 47. : Model predictive control of a two stage actuation system using piezoelectric actuators for controllable industrial and automotive brakes and clutches. J. Intell. Mater. Syst. Struct. 19(7), 845–857 (2008) 48. : Stable control of an electro-hydraulic actuator during contact tasks: Theory and experiments. In: Proceeding of the American Control Conference, pp. 4114– 4118 (2000) 49. : Adaptive sliding-mode neuro-fuzzy control of the two-mass induction motor drive without mechanical sensors.

Afterward, the optimal model parameters can be predicted. 33) ei = yi − [wT ϕ(xi ) + b], i = 1, 2, . . 36) i=1 where μ is the new regularization factor and ζ denotes the variance of the noise for the residual error ei . 36 2 Feedforward Control Based on Inverse Hysteresis Models The dual program of the above optimization problem is the same as Eq. 27). The hyperparameter γ is related to μ and ζ by γ = ζ /μ. It is noticeable that, by substituting Eq. 35) and the above relationship into Eq. 33), the same problem as described by Eq.

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Advanced Control of Piezoelectric Micro-/Nano-Positioning Systems by Qingsong Xu, Kok Kiong Tan

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